Load Scheduling in Wideband Code Division Multiple Access

ABSTRACT

A method for load scheduling in a WCDMA communication system utilizing GRake equalizing radio reception comprises estimating ( 210 ) of channel estimates for a plurality of users. Combining weights are established ( 220 ) for a GRake equalizing reception for the present received uplink digital radio signals. Function parameters of a predicted future load measure function is predicted ( 230 ) as a function of individual grants of the plurality of users based on at least the channel estimates and the combing weights, taking sensitivity for interference suppression provided by the GRake equalizing for each of the plurality of users into account. The predicting further comprises adaptation of the function parameters for changed load equilibrium levels caused by the individual grants of the plurality users. A set of grants for the users is selected ( 240 ) based on the future load measure function. Uplink load is scheduled ( 250 ) according to the selected set of grants.

TECHNICAL FIELD

The present invention relates in general to devices and methods for loadscheduling in Wideband Code Division Multiple Access (WCDMA) systems,and in particular to load scheduling in systems utilizing GRakeequalizing radio receivers.

BACKGROUND

WCDMA technology provides well established techniques for spectralutilization in high load mobile communication systems. InterferenceCancellation (IC) and Interference Suppression (IS) may be used in WCDMAsystems in order to achieve better performance in terms of e.g. peakdata rates, coverage, system throughput and system capacity. IC and ISare applicable both for DownLink (DL) and UpLink (UL). However, mostload limiting parameters that are difficult to control are connectedwith the UL signaling.

The basic idea behind IS is to combine the received radio signals suchthat interference is suppressed and the Signal-to-Interference-and-NoiseRatio (SINR) is maximized. There are many ways to achieve IS accordingto prior art. Non-exclusive examples are interference rejectioncombining, where the signals from more than one antenna are combined inorder to suppress interference, and Generalized Rake+ (GRake+) (alsoreferred to as non-parametric GRake), where interference is suppressedby whitening of the interference both in the temporal and the spatialdomain.

Even though there exist several already known ways to achieve IS at linklevel, the knowledge of how to utilize the link level gain in order toincrease the capacity or cell throughput in a WCDMA network is limited.

It first needs to be stressed that when advanced receivers such asGRake+ are applied, the conventional load measure without interferencesuppression is no longer valid. The conventional load measure is namelybased on the fact that each user affects all other users in exactly thesame way, from a load perspective, since conventional receivers do nothandle the interference from other users in any explicit way in thereceiver. However, with advanced receivers such as GRake+, a user'seffect on other users is not the same on all users, and the effect is afunction of the IC or IS efficiency.

Further, the load of the cell is used for scheduling e.g. of Enhanced UL(EUL) users, new and old. When doing this, the cell load measuredescribed for GRake+ provides a larger total scheduling headroom ascompared to pre-determined thresholds. However, the problem with thesolutions in prior art is that there is no technology in prior art thatallows the scheduler to address the detailed impact and contribution ofdifferent existing users, to the uplink Rise-over-Thermal (RoT) afterGRake+ processing, in the scheduling process.

SUMMARY

A general object of the present invention is to provide opportunities tobetter utilize the increased capacity obtained by GRake equalizing radioreception during scheduling of uplink traffic.

The above object is achieved by methods and devices according to theenclosed independent patent claims. Preferred embodiments are defined independent claims. In general words, in a first aspect, a method for loadscheduling in a WCDMA communication system utilizing GRake equalizingradio reception comprises estimating of channel estimates of presentreceived uplink digital radio signals for a plurality of users.Combining weights are established for a GRake equalizing reception forthe present received uplink digital radio signals. Function parametersof a predicted future load measure function are predicted. The predictedfuture load measure function is a function of individual grants of theplurality of users. The prediction is based on at least the estimatedchannel estimates and the established combing weights. The predictiontakes sensitivity for interference suppression provided by the GRakeequalizing for each of the plurality of users into account. Theprediction further comprises adaptation of the predicted functionparameters for changed load equilibrium levels caused by the individualgrants of the plurality users. A set of grants for the users is selectedgiving a required set of user future loads for the predicted future loadmeasure function defined by the predicted function parameters. Uplinkload is scheduled according to the selected set of grants.

In a second aspect, a scheduler arrangement in a WCDMA communicationsystem utilizing GRake equalizing radio reception comprises a channelestimator, an equalizer, a predictor, and a scheduler. The channelestimator is configured for estimating channel estimates of presentreceived uplink digital radio signals for a plurality of users. Theequalizer is configured for establishing combining weights for a GRakeequalizing receiver for the present received uplink digital radiosignals. The predictor is connected to the channel estimator and theequalizer. The predictor is configured for predicting functionparameters of a predicted future load measure. The predicted future loadmeasure function is a function of individual grants of the plurality ofusers. The predicting is based on at least the estimated channelestimates and the established combing weights taking sensitivity forinterference suppression provided by the GRake equalizing receiver foreach of the plurality of users into account. The predictor is furtherconfigured for adapting the predicted function parameters for changedload equilibrium levels caused by the individual grants of the pluralityusers. The scheduler is connected to the predictor. The scheduler isconfigured for selecting a set of grants for the users giving a requiredset of user future loads for the predicted future load measure functiondefined by the predicted function parameters. The scheduler is furtherconfigured for scheduling uplink load according to the selected set ofgrants.

In a third aspect, a node B in a WCDMA communication system utilizingGRake equalizing radio receivers comprises a scheduler arrangementaccording to the second aspect.

The present invention discloses one way to benefit from the IS gain on asystem level through scheduling in order to increase the capacity. Oneadvantage of the present invention is that the EUL scheduler will takethe load after GRake+ processing into account in the schedulingdecision. Other advantages are discussed in connection with differentembodiments described further below.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention, together with further objects and advantages thereof, maybest be understood by making reference to the following descriptiontaken together with the accompanying drawings, in which:

FIG. 1 is a schematic illustration of an example of a WCDMAcommunication system;

FIG. 2 is a schematic illustration of an embodiment of a schedulerarrangement;

FIGS. 3A and 3B are diagrams illustrating extrapolation of channelestimates and combining weights into future time;

FIG. 4 is a flow diagram of steps of an embodiment of a method for loadscheduling; and

FIG. 5 is a block diagram of an embodiment of a scheduler arrangement.

DETAILED DESCRIPTION

Throughout the drawings, the same reference numbers are used for similaror corresponding elements.

In the equations, vectors and matrices are generally denoted by boldsymbols.

In the following description, the terms GRake+ and non-parametric GRakeare used as synonyms, i.e. as equal and interchangeable terms.

The present invention relates to arrangements and methods in WCDMAcommunication systems. FIG. 1 illustrates a schematic view of anembodiment of such a WCDMA communication system 1. A node B 30communicates via its antenna/antennas 20 with a multitude of userequipments (UE) 10 situated within a cell 2 of the WCDMA communicationsystem 1. Radio signals transmitted from the node B 30 to the UEs 10 aredenoted as DL signals 14, and radio signals transmitted from the UEs 10to the node B 30 are denoted as UL signals 11. The present inventionmainly considers the UL signals, whereby arrangements for loadscheduling typically are provided in the node B 30 or connected thereto.Besides the intentional UL signals 11, the node B 30 also receivesinterfering signals 13 from UE's not presently handled by the Node B inquestion.

In prior art, different methods for determining a more reliable loadmeasure are available. These more reliable load measures give generallyincreased possibilities to schedule additional load. Some differentaspects of such methods are summarized in mathematical terms in AppendixA.

Approaches addressing ways to measure the WCDMA uplink load, as seenafter IS processing e.g. by a G-rake+ receiver, will be summarized inappendix B. That work proves that the load can be approximatelyexpressed as a noise rise over the thermal noise power floor (the socalled Rise-over-Thermal or RoT for short), individually for each user.The RoTs as seen by each of the users can then be combined into ameasure of the uplink cell load. Essentially, the user that experiencesthe worst interference after GRake+ processing becomes dimensioning forthe uplink cell load. Note that the technology disclosed in Appendix Bhence evaluates the RoT seen by each user, i.e. the impact of theinterference caused by the other users of the cell is addressed.

The load of the cell is typically used for admission of new users andscheduling of uplink EUL users, new and old. When doing scheduling, theabove load measure provides increased total scheduling headroom ascompared to pre-determined thresholds. However, there is no technologyin prior art that allows the scheduler to take the detailed impact andcontribution of different existing users, to the uplink RoT after GRake+processing, into account in the scheduling process.

In Appendix B, it has been demonstrated how an uplink cell load can becomputed in terms of the RoT after GRake+ and chip equalizer whiteningof the interference, as experienced by each user. It is noted that theinterference caused by a user on other users may have a very differenteffect on the RoT experienced by different users. However, the art asdescribed in Appendix B teaches how the RoT experienced by the users ofthe cell can be combined to a single cell load, preferably using theuser that experiences the worst RoT conditions.

Here below, it will be shown that a cell load measure may be constructedfrom user specific load measures and that certain actions may be takentowards specific users in order to increase the total cell capacity. Thepresent invention uses this knowledge and takes the additional step ofusing the cell load and user specific load measures and the capacityincreasing actions in the scheduling process in order to better utilizethe different types of GRake interference suppression gain to increasecell capacity.

In Appendix C, it is shown how to couple the RoT to the grants of allusers, using the power control loop. These calculations start from apresent load that can be calculated according to Appendix B. Under theassumption that the interference can be treated as generated by a whitenoise process, the interference for a user after GRake+ processing canbe related to the interference for the user before GRake+ processing bymeans of the combining weight vector. An assumption that the inner looppower control loop is closed after the GRake+ processing and that thecontrol objective is to keep the signal to noise ratio at the targetvalue makes it possible to deduce the effects of the inner loop. Thetarget value of the SINR can in a first approximation be consideredconstant, since it changes more slowly than both grants and inner looppower control quantities. A new equilibrium state can be found to acertain grant. The RoT can (c.f. C32) be expressed as a nonlinearfunction of the grants, where the function factors comprise e.g. theSINR target and the combining weights of the GRake+ processing. Thesequantities could in more elaborate embodiments be extrapolated to thefuture time when the new grants are to be implemented, from previous andpresent values.

Scheduling

The resulting load of a transmission after a scheduling decision in thefuture can now be calculated using (C32). Typically the schedulingpolicy is determined by optimization of a function of the grants,subject to the vector of constraints resulting from (C32) beingspecified to be below a threshold. Here many alternatives are possible,e.g. proportional fair and max Channel Quality Indicator (CQI)strategies well known in prior art.

To explain how this works, it can be assumed that the baseline schedulere.g. schedules users in a fair fashion regarding data rate. Theresulting user specific loads may then vary quite drastically. As amotivating example, a scheduled data rate of e.g. 1 Mbps each for threeusers in a cell may result in a user specific load of 1 dB as seen byone user in the cell, but another user may experience a 3 dB loadwhereas the third user may experience a load of 6 dB, where load isexpressed in RoT after GRake+ processing. The user experiencing thehighest load (6 dB in this example), will be limiting the cell capacity,which is assumed to be set to 6 dB in the example.

In an embodiment, the load after GRake+ is instead calculated in thescheduler before the actual transmission and used as a basis in thescheduling decision. What is needed to make the calculation is apparentfrom the equation (C32). The main point is that given that equation, anoptimal set of grants can be found that is consistent with theconstraints imposed by a threshold with respect to (C32).

In one embodiment, the scheduling criterion may be equal load.Continuing the example, the scheduler may e.g. determine, by using(C32), that if the three users are granted 0.5 Mbps, 1 Mbps and 3 Mbps,respectively, all users will experience a load of 6 dB in a RoT afterGRake+ sense. This means that the same scheduling threshold is met butthe total scheduled data rate increases from 3 Mbps to 4.5 Mbps. This isachieved by a search for the set of grants that gives the highestthroughput, while resulting in components of (C32) that are equal.

In other embodiments, other guiding scheduling principles may beapplied, e.g. targeting an equal data rate, which is similar to thebaseline solution, or water filling, where the user that causes thehighest load, as seen by other users, is given a low grant and a usersthat does not cause much load is given a high grant. Other principlesare also possible.

A more mathematical approach of two possible solutions of scheduling isgiven in Appendix D.

Once the scheduling is performed, the distribution of grants is madeaccording to well-known prior art routines.

FIG. 2 illustrates an embodiment of a scheduler arrangement 40. Such ascheduler arrangement 40 is typically provided in a node B, as e.g.illustrated n FIG. 1. The node B and scheduler arrangement 40 areconfigured for operating in a wideband code division multiple accesscommunication system utilizing GRake equalizing radio receivers. Thescheduler arrangement 40 comprises a channel estimator 50. The channelestimator 50 is configured for estimating channel estimates h of presentreceived uplink digital radio signals for a plurality of users. Theuplink digital radio signals are received at an input 41 to thescheduler arrangement 40. The scheduler arrangement 40 also comprises anequalizer 60, in this embodiment a GRake equalizer. The equalizer 60 isconfigured for establishing combining weights for a GRake equalizingreceiver for the present received uplink digital radio signals and istherefore also connected to the input 41. The equalizer 60 may,depending on the actual utilized approach, make use of the channelestimates of the present received uplink digital radio signals. Suchinformation is available through the channel estimator 50 and may beprovided therefrom, as indicated by the broken arrow 46. The equalizedsignal is provided on an output 42 to be utilized in other parts of thesystem, e.g. for decoding of the information contained in the signals.

The channel estimator 50 and the equalizer 60 may be configuredaccording to any prior art. The details of how these units operate arenot of primary importance for providing the benefits of the presentinvention, as long as they provide reliable channel estimates andequalizations, respectively.

The scheduler arrangement 40 further comprises a predictor 70. Thepredictor 70 is connected to the channel estimator for receiving thechannel estimates h 41. The predictor 70 is also connected to theequalizer 60 for receiving combining weights w 45 for the GRakeequalizing receiver. The predictor 70 is configured for predictingfunction parameters of a predicted future load measure function. Thepredicted future load measure function is a function of individualgrants of the plurality of users. This predicting of function parametersis based on at least the estimated channel estimates and the establishedcombing weights, as has been described here above. As also have beendescribed above, the predictor 70 is further configured for performingthe prediction of the function parameters taking sensitivity forinterference suppression provided by the GRake equalizing receiver foreach of the plurality of users into account. In other words, thepredictor incorporates the GRake equalizing effects into the predictionof future load situations, thereby enabling use of the benefits of theGRake equalizing for load scheduling purposes. The predictor 70 isfurthermore configured for adapting the predicted function parametersfor changed load equilibrium levels caused by the individual grants ofthe plurality users. The result is thus a future load measure functionRoT^(G+) 47.

The predicted future load measure function RoT^(G+) 47, typically in theform of a set of function parameters, is provided to a scheduler 80,i.e. the scheduler 80 is connected to the predictor 70. The scheduler 80is configured for selecting a set of grants for the users giving arequired set of user future loads for the predicted future load measurefunction 47 defined by the predicted function parameters. In otherwords, the scheduler 80 uses the predicted future load measure function47 to find a favorable set of grants that still fulfills some basicrequirements concerning user future loads. This can be performed in manydifferent ways. A simplest approach is to create a number of sets ofindividual grants, check which sets that will give rise to acceptableload situations and select the set of these acceptable sets that is“best” in some respect concerning the grant structure. Such selectionprocedures may also be performed in iterative manners, where a first“best” set of grants is used as a start for creating a next ensemble ofsets to be tested for finding an even better set of grants. Thepredicted future load measure function 47 itself can also be used,utilizing different kinds of optimization procedures to find an optimumof the function under certain constraints and according to certaincriteria. This was described more in detail above and in appendix D.

Thus, in a preferred embodiment, the scheduler 80 is further configuredfor optimizing a criterion function that is dependent on the individualgrants, and for selecting the individual grants giving the optimizationas the set of grants for the users. Then, the scheduler is preferablyconfigured for performing the optimization under a constraint relationinvolving the user future loads according to the predicted future loadmeasure function of individual grants.

The scheduler is further configured for scheduling uplink load accordingto the selected set of grants. The scheduling of grants γ is provided atan output 43.

The above procedure is based on the measured load of users. Thescheduler rather needs to use the predicted load for the same set ofusers. Hence one limitation of the basic embodiment is that the radioconditions should not vary too fast, say that they need to be stationaryfor several tens of milliseconds. A remedy to this situation would be touse extrapolation of the channel estimates over time, e.g. linearextrapolation, to provide a look ahead. FIG. 3A illustratesschematically such ideas. Channel estimates are provided continuously upto a present time t_(p), where a present channel estimate h_(p) isfound. If a future set of grants is assumed to be applied at the timet_(a), the channel estimate can be extrapolated from the previousbehavior to obtain an extrapolated channel estimate h_(a) at theapplication time t_(a). In other words, the predictor is furtherconfigured for extrapolating the channel estimates to a futureapplication time. The application time is a time when a present uplinkload scheduling is assumed to be applied.

If the difference between h_(p) and h_(a) is likely to be very small,such an extrapolation may not be necessary and the present channelestimate h_(p) may be used instead of the extrapolated channel estimateh_(a). This condition and solution should be reasonable at least forstationary mobile broadband users, which are anyway the ones likely touse high rates and produce the most interference.

The same is valid also for the combining weights for the GRakeequalizing receiver, as seen in FIG. 3B. Past and present values w_(p)of the combining weights can be utilized for extrapolating a futurecombining weight value w_(a) at the time of application of the set ofgrants that are to be determined. In other words, the predictor isfurther configured for extrapolating the combining weights to the futureapplication time.

FIG. 4 is a flow diagram of steps of an embodiment of a method for loadscheduling. The procedure for load scheduling in a wideband codedivision multiple access communication system utilizing GRake equalizingradio reception starts in step 200. In step 210, channel estimates ofpresent received uplink digital radio signals are estimated for aplurality of users. Combining weights for a GRake equalizing receptionfor the present received uplink digital radio signals are established instep 220. In step 230, function parameters of a predicted future loadmeasure function are predicted. The predicted future load measurefunction is a function of individual grants of the plurality of users.The predicting of function parameters is based on at least the estimatedchannel estimates and the established combing weights, takingsensitivity for interference suppression provided by the GRakeequalizing for each of the plurality of users into account. Thepredicting also comprises adapting the predicted function parameters forchanged load equilibrium levels caused by the individual grants of theplurality users. The future load measure function is preferably definedas described further above.

In step 240, a set of grants for the users is selected giving a requiredset of user future loads for the predicted future load measure functiondefined by the predicted function parameters. The step of selecting aset of grants preferably comprises optimizing of a criterion functionthat is dependent on the individual grants, and by selecting theindividual grants giving the optimization as the set of grants for theusers. Furthermore preferred, the optimization is performed under aconstraint relation involving the user future loads for the predictedfuture load measure function of the individual grants. Uplink load is instep 250 scheduling according to the selected set of grants. Theprocedure ends in step 299.

In preferred embodiments, the method further comprises extrapolating ofthe channel estimates to a future application time, where theapplication time is a time when a present uplink load scheduling isassumed to be applied. Likewise, in preferred embodiments, the methodfurther comprises extrapolating of the combining weights to the futureapplication time.

As an implementation example, FIG. 5 is a block diagram illustrating anexample embodiment of a scheduling arrangement 40. This embodiment isbased on a processor 93, for example a micro processor, a memory 94, asystem bus 90, an input/output (I/O) controller 92 and an I/O bus 91. Inthis embodiment the received uplink digital radio signals are receivedby the I/O controller 92 are stored in the memory 94. The I/O controller92 also controls the issue of the equalized uplink digital radio signalsand the scheduled set of grants. The processor 93 executes a softwarecomponent 95 for performing a channel estimation on the received uplinkdigital radio signal, and a software component 96 for equalizing thereceived uplink digital radio signal. The processor 93 executes asoftware component 97 for predicting function parameters of a predictedfuture load measure function, a software component 98 for selecting aset of grants, and a software component 99 for scheduling of uplinkload. This software is stored in the memory 94. The processor 93communicates with the memory 94 over the system bus 90. Softwarecomponent 95 may implement the functionality of block 50 in theembodiment of FIG. 2. Software component 96 may implement thefunctionality of block 60 in the embodiment of FIG. 2. Softwarecomponent 97 may implement the functionality of block 70 in theembodiment of FIG. 2. Software component 98 and software component 99may implement the functionality of block 80 in the embodiment of FIG. 2.

As a summary, by utilizing the ideas of the present disclosures, theinterference suppression technique can be utilized properly and the cellcapacity and UL cell throughput will be possible to increase.

The embodiments described above are to be understood as a fewillustrative examples of the present invention. It will be understood bythose skilled in the art that various modifications, combinations andchanges may be made to the embodiments without departing from the scopeof the present invention. In particular, different part solutions in thedifferent embodiments can be combined in other configurations, wheretechnically possible. The scope of the present invention is, however,defined by the appended claims.

APPENDIX A

Load without IC/IS

It is e.g. shown in prior art that without IC/IS, the load at theantenna connector is given by the noise rise, or rise over thermal,RoT(t), defined by

$\begin{matrix}{{{{RoT}(t)} = \frac{{RTWP}(t)}{N(t)}},} & ({A1})\end{matrix}$

where N(t) is the thermal noise level as measured at the antennaconnector. It remains to define what is meant with RTWP(t). Thisrelative measure is unaffected of any de-spreading applied. Thedefinition used here is simply the Received Total Wideband Power:

$\begin{matrix}{{{{RTWP}(t)} = {{\sum\limits_{k = 1}^{K}{P_{k}(t)}} + {I^{N}(t)} + {N(t)}}},} & \left( {A\; 2} \right)\end{matrix}$

also measured at the antenna connector. Here I^(N)(t) denotes the poweras received from neighbor cells ^(N) of the WCDMA system. As will beseen below, the major difficulty of any RoT estimation algorithm is toseparate the thermal noise power from the interference from neighborcells.

Another specific problem that needs to be addressed is that the signalreference points are, by definition at the antenna connectors. Themeasurements are however obtained after the analogue signal conditioningchain, in the digital receiver. The analogue signal conditioning chaindoes introduce a scale factor error of about 1 dB (1-sigma) that isdifficult to compensate for. Fortunately, all powers of (2) are equallyaffected by the scale factor error so when (A1) is calculated, the scalefactor error is cancelled as

$\begin{matrix}{{{RoT}^{{Digital}\mspace{14mu} {Receiver}}(t)} = {\frac{{RTWP}^{{Digital}\mspace{14mu} {Receiver}}(t)}{N^{{Digitial}\mspace{14mu} {Receiver}}(t)} = {\frac{{\gamma (t)}{{RTWP}^{Antenna}(t)}}{{\gamma (t)}{N^{Antenna}(t)}} = {{{RoT}^{Antenna}(t)}.}}}} & ({A3})\end{matrix}$

In order to understand the fundamental problem of neighbor cellinterference when performing load estimation, note that:

I ^(N)(t)+N(t)=E[I ^(N)(t)]+E[N(t)]+ΔI ^(N)(t)+ΔN(t),  (A4)

where E[ ] denotes mathematical expectation and where Δ denotes thevariation around the mean. The fundamental problem can now be clearlyseen. Since there are no measurements available in the node B that arerelated to the neighbor cell interference, a linear filtering operationcan at best estimate the sum E[I^(N)(t)]+E[N(t)]. This estimate cannotbe used to deduce the value of E[N(t)]. The situation is the same aswhen the sum of two numbers is available. Then there is no way to figureout the values of the individual numbers. This issue is analyzedrigorously for the RoT estimation problem in prior art where it isproved that the noise power floor is not mathematically observable.

RoT Estimation Algorithms in Prior Art Sliding Window Algorithm

One RoT estimation algorithm according to prior art estimates the RoT,as given by (A1). The main problem solved by the estimation algorithm isthe accurate estimation of the thermal noise floor N(t). Since it is notpossible to obtain exact estimates of this quantity due to the neighborcell interference, the estimator therefore applies an approximation, byconsideration of the soft minimum as computed over a relative longwindow in time.

It is important to understand that this estimation relies on the factthat the noise floor is constant over very long periods of time(disregarding the small temperature drift).

Recursive Algorithm

The sliding window algorithm of the above section has the disadvantageof requiring a large amount of storage memory. This becomes particularlytroublesome in case a large number of instances of the algorithm isneeded, as may be the case when IC/IS is introduced in the uplink.

To reduce the memory consumption a recursive algorithm has beendisclosed in prior art. That algorithm reduces the memory requirementsof the sliding window scheme discussed above at least by a factor of100.

Cell Stability Oriented Load Estimation Algorithms in Prior Art

Some of the prior art cell stability load estimation functionality,exploits load factors for each user. In their simplest form the loadfactors are given by

$\begin{matrix}{{L_{u} = {\frac{P_{u}}{RTWP} = \frac{\left( {C/I} \right)_{u}}{1 + \left( {C/I} \right)_{u}}}},{u = 1},\ldots \mspace{14mu},U,} & ({A5})\end{matrix}$

where P_(u) is the power of user u. Load factors are then summed up, foreach power controlled user. In this way the neighbor cell interferenceis not included in the resulting load measure. This is reasonable sincethe neighbor cell interference should not affect the own cell powercontrol loop, at least not when first order effects are considered.

APPENDIX B

IC with Regeneration and Subtraction

The conventional procedure to perform IC is summarized by the followingsteps:

-   -   The channel of the interferer to be canceled is estimated. This        is anyway needed.    -   The transmitted signal of the interferer to be cancelled is        decoded. This is anyway needed.    -   A replica of the received signal of the interferer to be        cancelled is created, by use of the estimated channel and the        decoded signal. This replica may e.g. be reconstructed as an IQ        chip stream.    -   The replica of the interfering signal is subtracted from the        received signal of the user to be decoded, thereby hopefully        reducing the remaining power of the interferer to very low power        levels.

It is important to observe that the effect of this procedure isdifferent for different users, since an interferer is a user on its own.The consequence for load estimation is that there is no longer a uniformway to look on the interference of the WCDMA uplink—the load becomesindividual for each user. Hence combining user interference to an uplinkcell load is no longer trivial—rather it requires special measuresdisclosed below.

Finally, note that IC with regeneration and subtraction is morestraightforward than with GRake+ (treated below) since there is nochange of the scale factor for the thermal noise power floor. Theconsequence is that the RoT estimation algorithms are still applicablein this case, for each user, since a constant noise power level isestimated.

IS with G-Rake+ and Chip Equalizers

One difference with GRake+ as compared to conventional Rake, is thateach user sees a reduced level of interference, immediately after theweight combining step. In GRake+, a covariance matrix {circumflex over(R)}_(u), u=1, . . . , U, with the order equal to the number of fingersis first estimated to capture the interference. The spreading codes notused by the present user u may be used in order to estimate {circumflexover (R)}_(u).

The GRake+ receiver uses the estimated covariance matrix that models theinterference for computation of the combining weights for the users u,u=1, . . . , U.

{circumflex over (R)} _(u) ŵ _(u) =ĥ _(u) , u=1, . . . , U  (B1)

where ĥ_(u), u=1, . . . , U, is the net channel response of user u andwhere ŵ_(u) are the combining weights.

The effect of (B1) is that GRake+ essentially whitens the correlatedinterference and removes large spectral peaks from interferers atcertain finger locations and for certain antenna elements.

Note that GRake+ is still a linear receiver. There is a related type ofIS receiver for WCDMA which is also linear, denoted the chip equalizer.The difference between GRake+ and the chip equalizer is the order ofcertain basic operations. The consequence is that the present inventionis applicable to the chip equalizer as well.

Measurement of Load after IS in G-Rake+ and Chip Equalizers

To see how load can be estimated taking account of the GRake+ IS gain,the powers after weight combining are studied at sufficient statisticslevel. First, it is assumed that the received signal of user u on codekεΩ_(u) is:

y _(u,k) =h _(u) s _(u,k) +I _(u,k) +N _(u,k) , u=1, . . . , U, k=1, . .. , K  (B2)

where Ω_(u) denotes the set of codes for user u, s_(u,k), u=1, . . . ,U, k=1, . . . , K, is the signal, I_(u,k), u=1, . . . , U, k=1, . . . ,K, is the interference and N_(u,k), u=1, . . . , U, k=1, . . . , K, isthe (thermal) noise signal (not power) and ĥ_(u), u=1, . . . , U, is thenet channel response of user u. GRake+ then performs weight combining toget the sufficient statistics z_(u,k) ^(G+) according to the equations:

{circumflex over (z)} _(u,k) ^(G+) =ŵ _(u) ^(H) y _(u,k) =ŵ _(u) ^(H) ĥ_(u) s _(u,k) +ŵ _(u) ^(H) I _(u,k) +ŵ _(u) ^(H) N _(u,k) , u=1, . . . ,U, k=1, . . . , K.  (B3)

{circumflex over (R)} _(u) ŵ _(u) =ĥ _(u) , u=1, . . . , U  (B4)

Here ŵ_(u) are the combining weights of GRake+, whereas the estimatedcovariance matrix that models the interference for computation of thecombining weights for the users u is given by {circumflex over (R)}_(u).Equations (B3) and (B4) have two main implications; one indicating howpower measurements can be done and one indicating the scale factorproblem which is addressed below.

Using equation (B3) it can be seen that the effect of the GRake+ weightcombining is the same as if an artificial received signal z_(u,k) ^(G+)would be processed. Since these signals obviously reflect the weightcombining and thereby the IS gains of the GRake+ receiver, z_(u,k)^(G+), u=1, . . . , U, k=1, . . . , K, is believed to be a relevantstarting point for load estimation.

As stated above, the load estimator operates by processing of the RTWPand in the future possibly the received scheduled enhanced uplink powershared (RSEPS). For this reason, similar power signals need to be formedfrom the z_(u,k) ^(G+), u=1, . . . , U, k=1, . . . , K, in order toreuse the load concept applied without IS.

Note that it is not clear if the proposed approach to reuse the loadconcept applied without IS is precise or optimal.

User Powers Associated with the GRake+ Sufficient Statistics

Squaring (B3) and assuming a low degree of correlation between its threeterms, leads to:

|{circumflex over (z)} _(u,k) ^(G+)|² ≈ŵ _(u) ^(H) ĥ _(u) ĥ _(u) ^(H) ŵ_(u) |s _(u,k)|² +ŵ _(u) ^(H) I _(u,k) I _(u,k) ^(H) ŵ _(u) +ŵ _(u) ^(H)N _(u,k) N _(u,k) ^(H) ŵ _(u) ≡S _(u,k) ^(G+) +I _(u,k) ^(G+) +N _(u,k)^(G+) , u=1, . . . , U, k=1, . . . , K.  (B5)

The rise over thermal, as seen by user u is now, by definition:

$\begin{matrix}{{RoT}_{u}^{G +} \equiv \frac{S_{u}^{G +} + I_{u}^{G +} + N_{u}^{G +}}{N_{u}^{G +}}} & ({B6}) \\{S_{u}^{G +} = {\sum\limits_{k \in \Omega_{u}}S_{u,k}^{G +}}} & ({B7}) \\{I_{u}^{G +} = {\sum\limits_{k}I_{u,k}^{G +}}} & ({B8}) \\{N_{u}^{G +} = {\sum\limits_{k}{N_{u,k}^{G +}.}}} & ({B9})\end{matrix}$

Note that it is unclear how to distinguish between S_(u,k) ^(G+),I_(u,k) ^(G+) and N_(u,k) ^(G+) for kεΩ_(u). The algorithm disclosedhere avoids many of these problems, since both I_(u,k) ^(G+) and N_(u,k)^(G+) are computed from other quantities. Note further that in (B5)S_(u,k) ^(G+)=ŵ_(u) ^(H)ĥ_(u)ĥ_(u) ^(H)ŵ_(u)|s_(u,k)|², i.e. the poweris expressed starting with the (transmitted) code power |s_(u,k)|². Thesame quantity S_(u,k) ^(G+) can also be expressed starting with theantenna power |e_(u,k)|²=ĥ_(u) ^(H)ĥ_(u)|s_(u,k)|², in which caseS_(u,k) ^(G+)=ŵ_(u) ^(H)ŵ_(u)|e_(u,k)|². This latter setting is used inthe link simulations used for validation of the concept. The algorithmicdevelopment that follows does however use the definitions (B5)-(B9).

Computation of S_(u) ^(G+)

The signal power is computed directly from (B7). Using (B5) and (B7)then results in:

$\begin{matrix}{\begin{matrix}{S_{u}^{G +} = {\sum\limits_{k \in \Omega_{u}}S_{u,k}^{G +}}} \\{= {{\hat{w}}_{u}^{H}{\hat{h}}_{u}{\hat{h}}_{u}^{H}{\hat{w}}_{u}{\sum\limits_{k \in \Omega_{u}}{s_{u,k}}^{2}}}} \\{= {{\hat{w}}_{u}^{H}{\hat{h}}_{u}{\hat{h}}_{u}{\hat{w}}_{u}{\hat{E}}_{s,u}}} \\{{= {{{{\hat{w}}_{u}^{H}{\hat{h}}_{u}}}^{2}{\hat{E}}_{s,u}}},}\end{matrix}{{u = 1},\ldots \mspace{14mu},{U.}}} & ({B10})\end{matrix}$

Note that computation of the signal energy Ê_(s,u) is quite intricate,including e.g. the involved beta factors.

Computation of N_(u) ^(G+) White Noise Power Floor

The idea here is to rely on the baseline thermal noise power floorestimation algorithm to estimate the thermal noise power floor beforeany GRake+ processing. A main problem then arises since the thermalnoise is scaled by ŵ_(u) when the sufficient statistics is evaluated.This means that the thermal noise power level will no longer appearconstant.

The approach taken here to circumvent this problem builds on thecalculation of the scale factor by which the thermal noise power isscaled. To compute this quantity, first note that when the widebandthermal noise power floor is estimated before GRake+ processing, e.g.with the baseline noise floor estimator, the following quantity isestimated:

$\begin{matrix}\begin{matrix}{\hat{N} = {{\frac{1}{M}{\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K}{\left( N_{u,k}^{m} \right)^{H}N_{u,k}^{m}}}}}\underset{M->\infty}{->}{{KE}\left\lbrack {\left( N_{u,k} \right)^{H}N_{u,k}} \right\rbrack}}} \\{= {KP}_{{Nu},k}} \\{= {K\; \frac{1}{K}P_{N}}} \\{{= N_{0}},}\end{matrix} & ({B11})\end{matrix}$

where N₀ is the thermal noise power floor and where m is the samplesummation index. The power at the sufficient statistics signalprocessing point is however:

$\begin{matrix}\begin{matrix}{{\hat{N}}^{G +} = {\frac{1}{M}{\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K}{\left( {{\hat{w}}_{u}^{H}N_{u,k}^{m}} \right)^{H}{\hat{w}}_{u}^{H}N_{u,k}^{m}}}}}} \\{= {\frac{1}{M}{\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K}{{tr}\left( {\left( {{\hat{w}}_{u}^{H}N_{u,k}^{m}} \right)^{H}{\hat{w}}_{u}^{H}N_{u,k}^{m}} \right)}}}}} \\{= {\frac{1}{M}{\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K}{{tr}\left( {{\hat{w}}_{u}^{H}{N_{u,k}^{m}\left( {{\hat{w}}_{u}^{H}N_{u,k}^{m}} \right)}^{H}} \right)}}}}} \\{= {\frac{1}{M}{\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K}{{tr}\left( {{\hat{w}}_{u}^{H}{N_{u,k}^{m}\left( N_{u,k}^{m} \right)}^{H}{\hat{w}}_{u}} \right)}}}}} \\{= {{{tr}\left( {\sum\limits_{k = 1}^{K}{{{\hat{w}}_{u}^{H}\left( {\frac{1}{M}{\sum\limits_{m = 1}^{M}{N_{u,k}^{m}\left( N_{u,k}^{m} \right)}^{H}}} \right)}{\hat{w}}_{u}}} \right)}\underset{M->\infty}{->}}} \\{{{tr}\left( {K\; {\hat{w}}_{u}^{H}{E\left\lbrack {N_{u,k}\left( N_{u,k} \right)}^{H} \right\rbrack}{\hat{w}}_{u}} \right)}} \\{= {{tr}\left( {K{{\hat{w}}_{u}^{H}\left( {N_{0}/K} \right)}I{\hat{w}}_{u}} \right)}} \\{= {{\hat{w}}_{u}^{H}{\hat{w}}_{u}N_{0}}} \\{= {{\hat{w}}_{u}^{H}{\hat{w}}_{u}{\hat{N}.}}}\end{matrix} & ({B12})\end{matrix}$

The conclusion is that the thermal noise floor at the sufficientstatistics signal point can be obtained from the noise floor estimatebefore GRake+ processing, by a multiplication with the scale factor:

k _(u) ^(G+)=(ŵ _(u))^(H) ŵ _(u) , u=1, . . . , U.  (B13)

This gives:

N _(u) ^(G+) =k _(u) ^(G+) {circumflex over (N)}, u=1, . . . , U.  (B14)

The computation of the scale factor requires an additional inner productfor each user.

Colored Noise Power Floor

This subsection discusses the case where the result of (B11) is replacedby the more general assumption:

$\begin{matrix}{{{{\frac{1}{M}{\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K}{N_{u,k}^{m}\left( N_{u,k}^{m} \right)}^{H}}}}\underset{M->\infty}{->}{{KE}\left\lbrack {N_{u,k}\left( N_{u,k} \right)}^{H} \right\rbrack}} = {{K\; \frac{N_{0}}{K}R_{N}} = {N_{0}R_{N}}}},} & ({B15})\end{matrix}$

i.e. the case when sampling is fast enough to reflect the shape of theuplink spectrum. In this case it follows that (B11) is transformed to:

$\begin{matrix}\begin{matrix}{\hat{N} = {{\frac{1}{M}{\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K}{\left( N_{u,k}^{m} \right)^{H}N_{u,k}^{m}}}}}\underset{M->\infty}{->}{{KE}\left\lbrack {\left( N_{u,k} \right)^{H}N_{u,k}} \right\rbrack}}} \\{= {{Ktr}\left( {E\left\lbrack {N_{u,k}\left( N_{u,k} \right)}^{H} \right\rbrack} \right)}} \\{= {N_{0}{{tr}\left( R_{N} \right)}}}\end{matrix} & ({B16})\end{matrix}$

Furthermore, (B12) is transformed into

{circumflex over (N)} ^(G+) =N ₀ tr(ŵ _(u) ^(H) R _(N) ŵ _(u)).  (B17)

The end result in this case is the scale factor:

$\begin{matrix}{\kappa_{u}^{G +} = {\frac{{tr}\left( {{\hat{w}}_{u}^{H}R_{N}{\hat{w}}_{u}} \right)}{{tr}\left( R_{N} \right)}.}} & ({B18})\end{matrix}$

Computation of I_(u) ^(G+) Using Available SINRs

The code power to interference ratio is:

$\begin{matrix}{{\left( {C/I} \right)_{u\;}^{G +} = \frac{S_{u}^{G +}}{I_{u}^{G +} + N_{u}^{G +}}},{u = 1},\ldots \mspace{14mu},{U.}} & ({B19})\end{matrix}$

It can be noted that in (B19), all quantities except I_(u) ^(G+) havebeen computed, see (B12) and (B14). Using these quantities, (B19) can besolved for I_(u) ^(G+), giving:

$\begin{matrix}{{I_{u}^{G +} = {\frac{S_{u}^{G +}}{\left( {C/I} \right)_{u}^{G +}} - {\kappa_{u}^{G +}\hat{N}}}},{u = 1},\ldots \mspace{14mu},{U.}} & ({B20})\end{matrix}$

The quantity (C/I)_(u) ^(G+) can be directly related to SINR. This isperformed as:

$\begin{matrix}{{\left( {C/I} \right)_{u}^{G +} = {{\frac{\left( {\beta_{u,{DPCCH}}^{2} + \beta_{u,{EDPCCH}}^{2} + {n_{u,{codes}}\beta_{u,{EDPDCH}}^{2}}} \right)}{\beta_{u,{DPCCH}}^{2}{SF}_{u,{DPCCH}}}{SINR}_{u}^{G +}} = {\frac{\beta_{u,{effective}}^{2}}{{SF}_{u,{DPCCH}}}{SINR}_{u}^{G +}}}},} & ({B21})\end{matrix}$

which gives:

$\begin{matrix}{I_{u}^{G +} = {{\frac{S_{u}^{G +}}{\left( {C/I} \right)_{u}^{G +}} - {\kappa_{u}^{G +}\hat{N}}} = {{\frac{{SF}_{u,{DPCCH}}}{\beta_{u,{effective}}^{2}}\frac{S_{u}^{G +}}{{SINR}_{u}^{G +}}} - {\kappa_{u}^{G +}\hat{N}}}}} & ({B22})\end{matrix}$

Computation of RoT_(u) ^(G+)

When (B10), (B14) and (B22) are inserted in (B6), the end resultbecomes:

$\begin{matrix}{{{{RoT}_{u}^{G +} \equiv \frac{S_{u}^{G +} + I_{u}^{G +} + {\kappa_{u}^{G +}\hat{N}}}{\kappa_{u}^{G +}N}} = {\frac{S_{u}^{G +}}{\kappa_{u}^{G +}\hat{N}}\left( {1 + {\frac{{SF}_{u,{DPCCH}}}{\beta_{u,{effective}}^{2}}\frac{1}{{SINR}_{u}^{G +}}}} \right)}},{u = 1},\ldots \mspace{14mu},{U.}} & ({B23})\end{matrix}$

These measures, for each user, are then combined into an uplink measureas outlined below. Note that (B23) provides some interesting insights.When SINR is high then the RoT for the user is essentially determined bythe remaining own power of the user—the RoT then increases when the SINRgets worse.

Computation of RTWP and RSEPS Equivalents

The computation of the equivalent of RTWP and RSEPS power, at thesufficient statistics signal point, is discussed next. It follows from(B23) that the equivalent of RTWP, seen by user u, becomes:

$\begin{matrix}{{S_{u,{RTWP}}^{G +} = {S_{u}^{G +}\left( {1 + {\frac{{SF}_{u,{DPCCH}}}{\beta_{u,{effective}}^{2}}\frac{1}{{SINR}_{u}^{G +}}}} \right)}},{u = 1},\ldots \mspace{14mu},{U.}} & ({B24})\end{matrix}$

The equivalent of RSEPS, as seen by user u, is therefore obtained by asummation over the RSEPS user codes, when still using ĥ_(u) and ŵ_(u):

$\begin{matrix}{\mspace{79mu} {{S_{u,{RSEPS}}^{G +} = {\sum\limits_{u_{RSEPS} = 1}^{U_{RSEPS}}\; S_{u{(u_{RSPES})}}^{G +}}},{u = 1},\ldots \mspace{14mu},U}} & \left( {B\; 25} \right) \\{\begin{matrix}{S_{u{(u_{RSEPS})}}^{G +} = {{\sum\limits_{k \in \Omega_{u{(u_{RSEPS})}}}\; S_{u,k}^{G +}} = {{\hat{w}}_{u}^{H}{\hat{h}}_{u}{\hat{h}}_{u}^{H}{\hat{w}}_{u}{\sum\limits_{k \in \Omega_{u{(u_{RSEPS})}}}{s_{u,k}}^{2}}}}} \\{{= {{{\hat{w}}_{u}^{H}{\hat{h}}_{u}{\hat{h}}_{u}^{H}{\hat{w}}_{u}{\hat{E}}_{s,{u{(u_{RSPES})}}}} = {{{{\hat{w}}_{u}^{H}{\hat{h}}_{u}}}^{2}{\hat{E}}_{s,{u{(u_{RSEPS})}}}}}},}\end{matrix}\mspace{79mu} {{u_{RSEPS} = 1},\ldots \mspace{14mu},{U_{RSEPS}.}}} & \left( {B\; 26} \right)\end{matrix}$

Note again that the channel model of user u is retained when summingover the codes of the RSEPS users. Hence the computation needs to beperformed once for each user.

Uplink Load Measures for GRake+ Averaged Load Measure

Averaging over all users using (B23), gives the uplink load measure:

$\begin{matrix}{{\langle{RoT}^{G +}\rangle} = {\frac{1}{U}{\sum\limits_{u = 1}^{U}\; {{RoT}_{u}^{G +}.}}}} & \left( {B\; 27} \right)\end{matrix}$

This measure may not be suitable since it does not capture the effect ofusers with poor IS gain, these users being more likely to causeinstability by power increases. Similarly, the averaged RTWP and RSEPSmeasures become:

$\begin{matrix}{{{\langle S_{RTWP}^{G +}\rangle} = {\frac{1}{U}{\sum\limits_{u = 1}^{U}\; S_{u,{RTWP}}^{G +}}}}\;} & \left( {B\; 28} \right) \\{{\langle S_{RSEPS}^{G +}\rangle} = {\frac{1}{U}{\sum\limits_{u = 1}^{U}\; {S_{u,{RSEPS}}^{G +}.}}}} & \left( {B\; 29} \right)\end{matrix}$

Worst Case Load Measure

Rather than performing averaging a worst case approach may therefore betaken, where the averaging is replaced by a maximum operation. Thismeans that the user that sees the maximum total load is used for loadestimation purposes. This conservative approach could hence be motivatedby cell stability arguments—however it may also be too conservative. Theworst case load is defined by the equations:

$\begin{matrix}{u_{\max} = {\underset{u}{argmax}\left( {RoT}_{u}^{G +} \right)}} & \left( {B\; 30} \right) \\{{\max \left( {RoT}_{u}^{G +} \right)} = {RoT}_{u_{\max}}^{G +}} & \left( {B\; 31} \right) \\{{\max \left( S_{RTWP}^{G +} \right)} = S_{u_{\max},{RTWP}}^{G +}} & \left( {B\; 32} \right) \\{{\max \left( S_{RSEPS}^{G +} \right)} = S_{u_{\max},{RSEPS}}^{G +}} & \left( {B\; 33} \right)\end{matrix}$

Outage Interference Measure

A third alternative would be to sort the RoT_(u) ^(G+) and then selectthe user corresponding to a selected percentile.

APPENDIX C Reformulation of the Measured RoT Per User

In order to formulate the mathematical problem associated with theinvention, the equation giving the RoT per user needs to bereformulated. Towards this end, note that (26) gives

$\begin{matrix}{{\beta_{u,{effective}}^{2} = {{1 + \frac{\beta_{u,{EDPCCH}}^{2} + {n_{u,{codes}}\beta_{u,{EDPDCH}}^{2}}}{\beta_{u,{DCPCCH}}^{2}}} \equiv {1 + \gamma_{u}}}},{u = 1},\ldots \mspace{14mu},{U.}} & \left( {C\; 1} \right)\end{matrix}$

Using (B10), (B13) and (C1) in (B23) gives the following expression forthe RoT per user

$\begin{matrix}{{{{RoT}_{u}^{G +} \equiv \frac{S_{u}^{G +} + I_{u}^{G +} + {\kappa_{u}^{G +}\hat{N}}}{\kappa_{u}^{G +}\hat{N}}} = {\frac{{{{\hat{w}}_{u}^{H}{\hat{h}}_{u}}}^{2}E_{S,u}}{{\hat{w}}_{u}^{H}{\hat{w}}_{u}\hat{N}}\left( {1 + {\frac{{SF}_{u,{DPCCH}}}{1 + \gamma_{u}}\frac{1}{{SINR}_{u}^{G +}}}} \right)}},\mspace{79mu} {u = 1},\ldots \mspace{14mu},{U.}} & \left( {C\; 2} \right)\end{matrix}$

This expression depends on the channel ĥ_(u), the combining weightsŵ_(u), the total transmitted power from the UE E_(S,u), the noise floor{circumflex over (N)}, a spreading factor SF_(u,DPCCH), the granted datapower γ_(u), and the SINR SINR_(u) ^(G+), for the user at hand. Notethat the hat has been removed from E_(S,u) since the objective is not toestimate but to compute this quantity for scheduling purposes. It can benoted that the total transmit power of the UE is

E _(S,u)=(1+γ_(u))P _(u,DPCCH)(γ), u=1, . . . , U,  (C3)

which results in the following expression when inserted in (C2):

$\begin{matrix}{{{RoT}_{u}^{G +} = {\frac{{{{\hat{w}}_{u}^{H}{\hat{h}}_{u}}}^{2}{P_{u,{DPCCH}}(\gamma)}}{{\hat{w}}_{u}^{H}{\hat{w}}_{u}\hat{N}}\left( {1 + \gamma_{u} + {{SF}_{u,{DPCCH}}\frac{1}{{SINR}_{u}^{G +}}}} \right)}},\mspace{79mu} {u = 1},\ldots \mspace{14mu},{U.}} & \left( {C\; 4} \right)\end{matrix}$

The expression (C4) puts the development in a position to couple the RoTto the grants, using the power control loop. Note that the nonlinearcoupling of the Inner Loop Power Control (ILPC) makes the DedicatedPhysical Control CHannel (DPCCH) transmit power of a UE depend on thegrant of all users (γ). The grant vector is defined by (C25) below.

Interference Power after Interference Suppression

In order to come up with an expression for the interference power afterGRAKE+ processing (I_(u,neighbor) ^(G+)) the simplifying assumption thatthe interference can be treated as generated by a white noise process isnecessary.

Using this assumption the interference power after G-rake+ processingcan be treated as the thermal noise power floor, resulting in:

$\begin{matrix}{\begin{matrix}{{\hat{I}}_{u}^{G +} = {\frac{1}{M}{\sum\limits_{m = 1}^{M}\; {\sum\limits_{k = 1}^{K}\; {\left( {{\hat{w}}_{u}^{H}I_{u,k}^{m}} \right)^{H}{\hat{w}}_{u}^{H}I_{u,k}^{m}}}}}} \\{= {\frac{1}{M}{\sum\limits_{m = 1}^{M}\; {\sum\limits_{k = 1}^{K}\; {{tr}\left( {\left( {{\hat{w}}_{u}^{H}I_{u,k}^{m}} \right)^{H}{\hat{w}}_{u}^{H}I_{u,k}^{m}} \right)}}}}} \\{= {\frac{1}{M}{\sum\limits_{m = 1}^{M}\; {\sum\limits_{k = 1}^{K}\; {{tr}\left( {{\hat{w}}_{u}^{H}{I_{u,k}^{m}\left( {{\hat{w}}_{u}^{H}I_{u,k}^{m}} \right)}^{H}} \right)}}}}} \\{= {\frac{1}{M}{\sum\limits_{m = 1}^{M}\; {\sum\limits_{k = 1}^{K}\; {{tr}\left( {{\hat{w}}_{u}^{H}{I_{u,k}^{m}\left( I_{u,k}^{m} \right)}^{H}{\hat{w}}_{u}} \right)}}}}} \\{= {{tr}\left( {\sum\limits_{k = 1}^{K}{{{\hat{w}}_{u}^{H}\left( {\frac{1}{M}{\sum\limits_{m = 1}^{M}\; {I_{u,k}^{m}\left( I_{u,k}^{m} \right)}^{H}}} \right)}{\hat{w}}_{u}}} \right)}} \\{{\underset{M\rightarrow\infty}{\rightarrow}{{tr}\left( {{\hat{w}}_{u}^{H}{E\left\lbrack {I_{u}\left( I_{u} \right)}^{H} \right\rbrack}{\hat{w}}_{u}} \right)}}} \\{= {{tr}\left( {{{\hat{w}}_{u}^{H}\left( {\hat{I}}_{u} \right)}{\hat{w}}_{u}} \right)}} \\{= {{\hat{w}}_{u}^{H}{\hat{w}}_{u}{{\hat{I}}_{u}.}}}\end{matrix}{{u = 1},\ldots \mspace{14mu},{U.}}} & \left( {C\; 5} \right)\end{matrix}$

Since the objective again is computations involving the interferencepower, the hats are removed, resulting in:

I _(u) ^(G+) =w _(u) ^(H) w _(u) I _(u) , u=1, . . . , U.  (C6)

ILPC, RoT and Grants

The assumption of this development is that the inner loop power controlloop is closed after G-rake+ processing and that the control objectiveis to keep the SINR_(u) ^(G+) at the target value SINR_(u,reference)^(G+). The target value can be considered constant since it changesslower than both grants and ILPC quantities. Define the following vectorand matrix quantities:

$\begin{matrix}{{P_{DPCCH}^{G +} = \left( {P_{1,{DPCCH}}^{G +}\mspace{14mu} \ldots \mspace{14mu} P_{U,{DPCCH}}^{G +}}\; \right)^{T}},} & \left( {C\; 7} \right) \\{{P_{DPCCH} = \left( {P_{1,{DPCCH}}\mspace{14mu} \ldots \mspace{14mu} P_{U,{DPCCH}}}\; \right)^{T}},} & \left( {C\; 8} \right) \\{{I^{G +} = \left( {I_{1}^{G +}\mspace{14mu} \ldots \mspace{14mu} I_{U}^{G +}} \right)^{T}},} & \left( {C\; 9} \right) \\{{I = \left( {I_{1}\mspace{14mu} \ldots \mspace{14mu} I_{U}} \right)^{T}},} & \left( {C\; 10} \right) \\{{{\hat{N}}^{G +} = {\left( {1\mspace{14mu} \ldots \mspace{14mu} 1} \right)^{T}{\hat{N}}^{G +}}},} & \left( {C\; 11} \right) \\{{\hat{N} = {\left( {1\mspace{14mu} \ldots \mspace{14mu} 1} \right)^{T}\hat{N}}},} & \left( {C\; 12} \right) \\{{{SINR}^{G +} = \begin{pmatrix}{SINR}_{1}^{G +} & 0 & 0 \\0 & \ddots & 0 \\0 & 0 & {SINR}_{U}^{G +}\end{pmatrix}},} & \left( {C\; 13} \right) \\{{{SINR}_{reference}^{G +} = \begin{pmatrix}{SINR}_{1,{reference}}^{G +} & 0 & 0 \\0 & \ddots & 0 \\0 & 0 & {SINR}_{U,{reference}}^{G +}\end{pmatrix}},} & \left( {C\; 14} \right) \\{{SF}_{DPCCH} = \begin{pmatrix}{SF}_{1,{DPCCH}} & 0 & 0 \\0 & \ddots & 0 \\0 & 0 & {SF}_{U,{DPCCH}}\end{pmatrix}} & \left( {C\; 15} \right) \\{{W_{{w}^{2}} = \begin{pmatrix}{w_{1}^{H}w_{1}} & 0 & 0 \\0 & \ddots & 0 \\0 & 0 & {w_{U}^{H}w_{U}}\end{pmatrix}},} & \left( {C\; 16} \right) \\{W_{{{w^{H}h}}^{2}} = {\begin{pmatrix}{{w_{1}^{H}h_{1}}} & 0 & 0 \\0 & \ddots & 0 \\0 & 0 & {{w_{U}^{H}h_{U}}}^{2}\end{pmatrix}.}} & \left( {C\; 17} \right)\end{matrix}$

It then follows from the definitions of C/I (B20), and SINR (B21), and(C1) that:

I ^(G+) =SF _(DPCCH)(SINR^(G+))⁻¹ P _(DPCCH) ^(G+) −{circumflex over(N)} ^(G+).  (C18)

Noting that (B10) shows that

P _(DPCCH) ^(G+) =W _(w) _(H) _(h|) ₂ P _(DPCCH),  (C19)

and that (B12) and (C6) imply:

{circumflex over (N)} ^(G+) =W _(|w|) ₂ {circumflex over (N)},  (C20)

Î ^(G+) =W _(|w|) ₂ I.  (C21)

Using the fact that the ILPC is assumed to operate correctly, i.e. thatthe SINR operating point equals the SINR reference and the fact that theloop is closed after G-rake+ processing, it follows that:

I=W _(|w|) ₂ ⁻¹ SF _(DPCCH)(SINR_(reference) ^(G+))⁻¹ W _(|w) _(H) _(h|)₂ P _(DPCCH) −{circumflex over (N)}.  (C22)

It remains to relate the interference to the powers, to compute theequilibrium solution. Towards that end, it is noted that before thereceiver:

I=R(γ)H _(|h|) ₂ P _(DPCCH),  (C23)

where

$\begin{matrix}{{{R(\gamma)} = \begin{pmatrix}{\alpha_{1}\left( {1 + \gamma_{1}} \right)} & {1 + \gamma_{2}} & {1 + \gamma_{3}} & \ldots & {1 + \gamma_{U}} \\{1 + \gamma_{1}} & {\alpha_{2}\left( {1 + \gamma_{2}} \right)} & {1 + \gamma_{3}} & \ldots & {1 + \gamma_{U}} \\{1 + \gamma_{1}} & {1 + \gamma_{2}} & {\alpha_{3}\left( {1 + \gamma_{3}} \right)} & \ldots & {1 + \gamma_{U}} \\\vdots & \vdots & \vdots & \ddots & \vdots \\{1 + \gamma_{1}} & {1 + \gamma_{2}} & {1 + \gamma_{3}} & \ldots & {\alpha_{U}\left( {1 + \gamma_{U}} \right)}\end{pmatrix}},} & \left( {C\; 24} \right) \\{\mspace{79mu} {{\gamma = \left( {\gamma_{1}\mspace{14mu} \ldots \mspace{14mu} \gamma_{U}} \right)^{T}},}} & \left( {C\; 25} \right) \\{\mspace{79mu} {H_{{h}^{2}} = {\begin{pmatrix}{h_{1}}^{2} & 0 & 0 \\0 & \ddots & 0 \\0 & 0 & {h_{U}}^{2}\end{pmatrix}.}}} & \left( {C\; 26} \right)\end{matrix}$

Above, α_(u), u=1, . . . , U, denotes the self interference factors.Inserting (C23) into (C22) renders the following equation for thetransmit powers:

R(γ)H _(|h|) ₂ P _(DPCCH) =W _(|w|) ₂ ⁻¹ SF _(DPCCH)(SINR_(reference)^(G+))⁻¹ W _(|w) _(H) _(h|) ₂ P _(DPCCH) −{circumflex over (N)}.  (C27)

The solution is:

P _(DPCCH)=(W _(|w|) ₂ ⁻¹ SF _(DPCCH)(SINR_(reference) ^(G+))⁻¹ W _(|w)_(H) _(h|) ₂ −R(γ)H _(|h|) ₂ )⁻¹ {circumflex over (N)}.  (C28)

With the DPCCH transmit powers computed in (C28), the RoT computationcan be finalized by insertion of (C28) in a vectorized version of (C4).It is easy to see that the vectorized version of (C4) can be written as:

$\begin{matrix}{{{{RoT}^{G +}(\gamma)} = {\frac{1}{\hat{N}}W_{{{w^{H}h}}^{2}}{W_{{w}^{2}}^{- 1}\left( {I + {{diag}(\gamma)} + {{SF}_{DPCCH}\left( {SINR}_{reference}^{G +} \right)}^{- 1}} \right)}P_{DPCCH}}},} & \left( {C\; 29} \right)\end{matrix}$

where I is the identity matrix, and where:

$\begin{matrix}{{{{diag}(\gamma)} = \begin{pmatrix}\gamma_{1} & 0 & 0 \\0 & \ddots & 0 \\0 & 0 & \gamma_{U}\end{pmatrix}},} & \left( {C\; 30} \right) \\{{SF}_{DPCCH} = {\begin{pmatrix}{SF}_{1,{DPCCH}} & 0 & 0 \\0 & \ddots & 0 \\0 & 0 & {SF}_{U,{DPCCH}}\end{pmatrix}.}} & \left( {C\; 31} \right)\end{matrix}$

Combining (C12), (C28) and (C29) results in:

RoT^(G+)(γ)=W _(|w) _(H) _(h|) ₂ W _(|w|) ₂ ⁻¹(I+diag(γ)+SF_(DPCCH)(SINR_(reference) ^(G+))⁻¹)·(W _(|w|) ₂ ⁻¹ SF_(DPCCH)(SINR_(reference) ^(G+))⁻¹ W _(|w) _(H) _(h|) ₂ −R(γ)H _(|h|) ₂)⁻¹1,  (C32)

where 1 is a vector of ones, c.f. (C12). As can be seen the RoT is anonlinear function of the grants.

APPENDIX D Example 1 of Scheduling Calculations

The formulation of the problem to be solved:

Maximize the sum of the granted powers (these are approximatelyproportional to the bit rates), while keeping the pre-scribed RoT.

This is expressed as:

$\begin{matrix}{{\max\limits_{\gamma}\left( {{trace}\left( {{diag}(\gamma)} \right)} \right)},} & \left( {D\; 1} \right)\end{matrix}$

subject to:

RoT^(G+)(γ)=W _(|w) _(H) _(h|) ₂ W _(|w|) ₂ ⁻¹(I+diag(γ)+SF_(DPCCH)(SINR_(reference) ^(G+))⁻¹)·(W _(|w|) ₂ ⁻¹ SF_(DPCCH)(SINR_(reference) ^(G+))⁻¹ W _(|w) _(H) _(h|) ₂ −R(γ)H _(|h|) ₂)⁻¹1≦RoT_(max) ^(G+)1  (D2)

To solve the problem (D1)-(D2) standard algorithms from optimizationtheory, see e.g. D. G. Luenberger, Linear and Nonlinear Programming,2:nd ed., Addison Wesley, 1984, can be used. The problem (D1)-(D2) is aproblem with a linear criterion, subject to a nonlinear inequalityconstraint. Such problems can e.g. be solved by techniques detailed inthe book by Luenberger. Examples include the active set method (section11.3), the gradient projection method (section 11.4), the barrier method(section 12.2) and the cutting plane method (section 13.6).

Example 2 of Scheduling Calculations

The formulation of the problem to be solved:

Select the granted powers (these are approximately proportional to thebit rates) so that the RoT for all users achieve the maximum RoT. Theall users are worst case users which should represent a case with highbit rates.

This is expressed as:

RoT^(G+)(γ)=W _(|w) _(H) _(h|) ₂ W _(|w|) ₂ ⁻¹(I+diag(γ)+SF_(DPCCH)(SINR_(reference) ^(G+))⁻¹)·(W _(|w|) ₂ ⁻¹ SF_(DPCCH)(SINR_(reference) ^(G+))⁻¹ W _(|w) _(H) _(h|) ₂ −R(γ)H _(|h|) ₂)⁻¹1=RoT_(max) ^(G+)1  (D3)

To solve the problem (D3), a system of nonlinear equations needs to besolved for γ. Again the book by Luenberger advices many methods, e.g.the steepest descent and the Newton method, see sections 7.6 and 7.8,respectively.

ABBREVIATIONS CQI Channel Quality Indicator DL DownLink DPCCH DedicatedPhysical Control CHannel EUL Enhanced UpLink GRake Generalized Rake ICInterference Cancellation ILPC Inner Loop Power Control I/O Input/outputIS Interference Suppression RoT Rise-over-Thermal

RSEPS Received Scheduled Enhanced uplink Power Shared

RTWP Received Total Wideband Power SINR Signal-to-Interference-and-NoiseRatio UE User Equipment UL UpLink

WCDMA Wideband Code Division Multiple Access

1-13. (canceled)
 14. A method for load scheduling in a wideband codedivision multiple access communication system utilizing GRake equalizingradio reception on an uplink, comprising: estimating channel estimatesof present received uplink digital radio signals for a plurality ofusers; establishing combining weights for GRake equalizing reception ofsaid present received uplink digital radio signals; predicting functionparameters of a predicted future load measure function based on at leastsaid channel estimates and said combing weights, wherein said predictedfuture load measure function is a function of individual grants to bemade to said users for a future scheduling time; adapting said predictedfunction parameters for changed load equilibrium levels caused by saidindividual grants of said users; selecting a set of grants to be made tothe users for said future scheduling time that gives a required set ofuser future loads for said predicted future load measure function; andscheduling uplink load according to said selected set of grants.
 15. Themethod according to claim 14, further comprising extrapolating saidchannel estimates to a future application time, said application timebeing a future time when a present uplink load scheduling is assumed tobe applied.
 16. The method according to claim 14, further comprisingextrapolating said combining weights to a future application time, saidapplication time being a future time when a present uplink loadscheduling is assumed to be applied.
 17. The method according to claim14, wherein selecting the set of grants comprises optimizing a criterionfunction that is dependent on said individual grants, and selecting theindividual grants giving said optimization as said set of grants for theusers.
 18. The method according to claim 17, wherein said optimizationis performed under a constraint relation involving said user futureloads for said predicted future load measure function of individualgrants.
 19. The method according to claim 14, wherein said future loadmeasure function is defined as:RoT^(G+)(γ)=W _(|w) _(H) _(h|) ₂ W _(|w|) ₂ ⁻¹(I+diag(γ)+SF_(DPCCH)(SINR_(reference) ^(G+))⁻¹)·(W _(|w|) ₂ ⁻¹ SF_(DPCCH)(SINR_(reference) ^(G+))⁻¹ W _(|w) _(H) _(h|) ₂ −R(γ)H _(|h|) ₂)⁻¹1; where function parameter W_(|w) _(H) _(h|) ₂ is a diagonal matrixwith diagonal elements of |w_(n) ^(H)h_(n)|², n=1 . . . U, where U isthe number of said plurality of users, w_(n) is the combining weight ofuser n, h_(n) is the channel estimate of user n; where functionparameter W_(|w|) ₂ is a diagonal matrix with diagonal elements of w_(n)^(H)w_(n), n=1 . . . U; where I is the identity matrix; where diag(γ) isa diagonal matrix with the individual grants γ_(n), n=1 . . . U of saidplurality of users as diagonal elements; where function parameterSF_(DPCCH) is a diagonal matrix with the scrambling factors of adedicated physical control channel of said plurality of users asdiagonal elements; where function parameter SINR_(reference) ^(G+) is areference signal to interference and noise ratio after application ofGRake equalizing for said plurality of users as diagonal elements;where: ${{R(\gamma)} = \begin{pmatrix}{\alpha_{1}\left( {1 + \gamma_{1}} \right)} & {1 + \gamma_{2}} & {1 + \gamma_{3}} & \ldots & {1 + \gamma_{U}} \\{1 + \gamma_{1}} & {\alpha_{2}\left( {1 + \gamma_{2}} \right)} & {1 + \gamma_{3}} & \ldots & {1 + \gamma_{U}} \\{1 + \gamma_{1}} & {1 + \gamma_{2}} & {\alpha_{3}\left( {1 + \gamma_{3}} \right)} & \ldots & {1 + \gamma_{U}} \\\vdots & \vdots & \vdots & \ddots & \vdots \\{1 + \gamma_{1}} & {1 + \gamma_{2}} & {1 + \gamma_{3}} & \ldots & {\alpha_{U}\left( {1 + \gamma_{U}} \right)}\end{pmatrix}},$ where function parameters α_(n), n=1 . . . U are selfinterference factors of said plurality of users; where functionparameter H_(|h|) ₂ is a diagonal matrix with diagonal elements of|h_(n)|², n=1 . . . U; and where 1 is a vector of ones.
 20. A schedulerarrangement in a wideband code division multiple access communicationsystem utilizing GRake equalizing radio reception on an uplink,comprising: a channel estimator configured to estimate channel estimatesof present received uplink digital radio signals for a plurality ofusers; an equalizer configured to establish combining weights for GRakeequalizing reception of said present received uplink digital radiosignals; a predictor that is connected to said channel estimator andsaid equalizer, and that is configured to: predict function parametersof a predicted future load measure function based on at least saidchannel estimates and said combing weights, wherein said predictedfuture load measure function is a function of individual grants to bemade to said users for a future scheduling time; and adapt saidpredicted function parameters for changed load equilibrium levels causedby said individual grants of said users; and a scheduler that isconnected to said predictor and that is configured to: select a set ofgrants for the users that gives a required set of user future loads forsaid predicted future load measure function defined by said predictedfunction parameters; and schedule uplink load according to said selectedset of grants.
 21. The scheduler arrangement according to claim 20,wherein said predictor is further configured to extrapolate said channelestimates to a future application time, said application time being afuture time when a present uplink load scheduling is assumed to beapplied.
 22. The scheduler arrangement according to claim 20, whereinsaid predictor is further configured to extrapolate said combiningweights to a future application time, said application time being afuture time when a present uplink load scheduling is assumed to beapplied.
 23. The scheduler arrangement according to claim 20, whereinsaid scheduler is further configured to optimize a criterion functionthat is dependent on said individual grants, and to select theindividual grants giving said optimization as said set of grants for theusers.
 24. The scheduler arrangement according to claim 23, wherein saidscheduler is further configured to perform said optimization under aconstraint relation involving said user future loads for said predictedfuture load measure function of individual grants.
 25. The schedulerarrangement according to claim 20, wherein said predictor is configuredto use said future load measure function defined as:RoT^(G+)(γ)=W _(|w) _(H) _(h|) ₂ W _(|w|) ₂ ⁻¹(I+diag(γ)+SF_(DPCCH)(SINR_(reference) ^(G+))⁻¹)·(W _(|w|) ₂ ⁻¹ SF_(DPCCH)(SINR_(reference) ^(G+))⁻¹ W _(|w) _(H) _(h|) ₂ −R(γ)H _(|h|) ₂)⁻¹1; where function parameter W_(|w) _(H) _(h|) ₂ is a diagonal matrixwith diagonal elements of w_(n) ^(H)h_(n)|², n=1 . . . U, where U is thenumber of said plurality of users, w_(n) is the combining weight of usern, h_(n) is the channel estimate of user n; where function parameterW_(|w|) ₂ is a diagonal matrix with diagonal elements of w_(n)^(H)w_(n), n=1 . . . U; where I is the identity matrix; where diag(γ) isa diagonal matrix with the individual grants γ_(n), n=1 . . . U of saidplurality of users as diagonal elements; where function parameterSF_(DPCCH) is a diagonal matrix with the scrambling factors of adedicated physical control channel of said plurality of users asdiagonal elements; where function parameter SINR_(reference) ^(G+) is areference signal to interference and noise ratio after application ofGRake equalizing for said plurality of users as diagonal elements;where: ${{R(\gamma)} = \begin{pmatrix}{\alpha_{1}\left( {1 + \gamma_{1}} \right)} & {1 + \gamma_{2}} & {1 + \gamma_{3}} & \ldots & {1 + \gamma_{U}} \\{1 + \gamma_{1}} & {\alpha_{2}\left( {1 + \gamma_{2}} \right)} & {1 + \gamma_{3}} & \ldots & {1 + \gamma_{U}} \\{1 + \gamma_{1}} & {1 + \gamma_{2}} & {\alpha_{3}\left( {1 + \gamma_{3}} \right)} & \ldots & {1 + \gamma_{U}} \\\vdots & \vdots & \vdots & \ddots & \vdots \\{1 + \gamma_{1}} & {1 + \gamma_{2}} & {1 + \gamma_{3}} & \ldots & {\alpha_{U}\left( {1 + \gamma_{U}} \right)}\end{pmatrix}},$ where function parameter α_(n), n=1 . . . U are selfinterference factors of said plurality of users; where functionparameter H_(|h|) ₂ is a diagonal matrix with diagonal elements of|h_(n)|², n=1 . . . U; and where 1 is a vector of ones.
 26. A node B ina wideband code division multiple access communication system utilizingGRake equalizing radio receivers on an uplink, said node B comprising ascheduler arrangement, said scheduler arrangement comprising: a channelestimator configured to estimate channel estimates of present receiveduplink digital radio signals for a plurality of users; an equalizerconfigured to establish combining weights for GRake equalizing receptionof said present received uplink digital radio signals; a predictor thatis connected to said channel estimator and said equalizer, and that isconfigured to: predict function parameters of a predicted future loadmeasure function based on at least said channel estimates and saidcombing weights, wherein said predicted future load measure function isa function of individual grants to be made to said users for a futurescheduling time; and adapt said predicted function parameters forchanged load equilibrium levels caused by said individual grants of saidusers; and a scheduler that is connected to said predictor and that isconfigured to: select a set of grants for the users that gives arequired set of user future loads for said predicted future load measurefunction defined by said predicted function parameters; and scheduleuplink load according to said selected set of grants.
 27. The node Baccording to claim 26, wherein said predictor is further configured toextrapolate said channel estimates to a future application time, saidapplication time being a future time when a present uplink loadscheduling is assumed to be applied.
 28. The node B according to claim26, wherein said predictor is further configured to extrapolate saidcombining weights to a future application time, said application timebeing a future time when a present uplink load scheduling is assumed tobe applied.
 29. The node B according to claim 26, wherein said scheduleris further configured to optimize a criterion function that is dependenton said individual grants, and to select the individual grants givingsaid optimization as said set of grants for the users.
 30. The node Baccording to claim 29, wherein said scheduler is further configured toperform said optimization under a constraint relation involving saiduser future loads for said predicted future load measure function ofindividual grants.
 31. The node B according to claim 26, wherein saidpredictor is configured to use said future load measure function definedas:RoT^(G+)(γ)=W _(|w) _(H) _(h|) ₂ W _(|w|) ₂ ⁻¹(I+diag(γ)+SF_(DPCCH)(SINR_(reference) ^(G+))⁻¹)·(W _(|w|) ₂ ⁻¹ SF_(DPCCH)(SINR_(reference) ^(G+))⁻¹ W _(|w) _(H) _(h|) ₂ −R(γ)H _(|h|) ₂)⁻¹1; where function parameter W_(|w) _(H) _(h|) ₂ is a diagonal matrixwith diagonal elements of |w_(n) ^(H)h_(n)|², n=1 . . . U, where U isthe number of said plurality of users, w_(n) is the combining weight ofuser n, h_(n) is the channel estimate of user n; where functionparameter W_(|w|) ₂ is a diagonal matrix with diagonal elements of w_(n)^(H)w_(n), n=1 . . . U; where I is the identity matrix; where diag(γ) isa diagonal matrix with the individual grants γ_(n), n=1 . . . U of saidplurality of users as diagonal elements; where function parameterSF_(DPCCH) is a diagonal matrix with the scrambling factors of adedicated physical control channel of said plurality of users asdiagonal elements; where function parameter SINR_(reference) ^(G+) is areference signal to interference and noise ratio after application ofGRake equalizing for said plurality of users as diagonal elements;where: ${{R(\gamma)} = \begin{pmatrix}{\alpha_{1}\left( {1 + \gamma_{1}} \right)} & {1 + \gamma_{2}} & {1 + \gamma_{3}} & \ldots & {1 + \gamma_{U}} \\{1 + \gamma_{1}} & {\alpha_{2}\left( {1 + \gamma_{2}} \right)} & {1 + \gamma_{3}} & \ldots & {1 + \gamma_{U}} \\{1 + \gamma_{1}} & {1 + \gamma_{2}} & {\alpha_{3}\left( {1 + \gamma_{3}} \right)} & \ldots & {1 + \gamma_{U}} \\\vdots & \vdots & \vdots & \ddots & \vdots \\{1 + \gamma_{1}} & {1 + \gamma_{2}} & {1 + \gamma_{3}} & \ldots & {\alpha_{U}\left( {1 + \gamma_{U}} \right)}\end{pmatrix}},$ where function parameter α_(n), n=1 . . . U are selfinterference factors of said plurality of users; where functionparameter H_(|h|) ₂ is a diagonal matrix with diagonal elements of|h_(n)|², n=1 . . . U; and where 1 is a vector of ones.
 32. A method forload scheduling in a wideband code division multiple accesscommunication system utilizing GRake equalizing radio reception,comprising: determining channel estimates and GRake combining weightsfor a plurality of users, based on uplink digital radio signals receivedfrom the users; expressing a target loading as a function of the channelestimates and the GRake combining weights and further as a function oftransmit powers proportional to individual grants to be made to theplurality of users for a future scheduling time; and determining a setof individual grants to use for the future scheduling time, for theplurality of users, based on determining the transmit powers thatmaximize throughput for the future scheduling time while maintainingequilibrium with the target loading.